Novel urinary metabolite signature for diagnosing postpartum depression

Lin, Lin; Chen, Xiaomei; Liu, Ronghua

doi:10.2147/ndt.s135190

Cited by 28 publications

(20 citation statements)

References 37 publications

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“…However, the OPLS-DA model (multivariate analysis) still viewed these metabolites as differential metabolites responsible for the discrimination between the two groups. Previous metabolic studies also reported the similar results [ 27 , 28 ]. This was because the multivariate analysis found the highest discrimination power after adding these metabolites into the discrimination model.…”

Section: Discussionsupporting

confidence: 85%

GC-MS-based metabolomics approach to diagnose depression in hepatitis B virus-infected patients with middle or old age

Hou

Wei

Zhuo

et al. 2018

Aging

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Depression is concomitantly presented in hepatitis B virus (HBV)-infected patients (HB). However, there is still no objective method to diagnose HBV-infected patients with depression (dHB). Therefore, in this study, a gas chromatography-mass spectrometry (GC-MS)-based metabolomic approach was employed to profile urine samples from 59 dHB and 52 HB (the training set) in order to identify urinary metabolite biomarkers for dHB. Then, 41 dHB and 35 HB (the testing set) were used to independently validate the diagnostic generalizability of these biomarkers. In total, 13 differential metabolites responsible for the discrimination between dHB and HB were identified. These differential urinary metabolites belonged mainly to Lipid metabolism and Amino acid metabolism. A panel consisting of six urinary metabolite biomarkers (ethanolamine, azelaic acid, histidine, threitol, 2,4-dihydroxypyrimidine and levulinic acid) was identified. This panel was capable of distinguishing dHB from HB with an area under the receiver operating characteristic curve (AUC) of 0.986 in the training set. Moreover, this panel could classify blinded samples from the testing set with an AUC of 0.933. These findings indicated that the GC-MS-based metabolomics approach could be a useful tool in the clinical diagnosis of dHB, and the identified biomarkers were helpful for future developing an objective diagnostic method for dHB.

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Section: Discussionsupporting

confidence: 85%

GC-MS-based metabolomics approach to diagnose depression in hepatitis B virus-infected patients with middle or old age

Hou

Wei

Zhuo

et al. 2018

Aging

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“…Among the four biomarkers identified here, the significantly decreased N -methylnicotinamide and hippuric acid levels, and increased azelaic acid level, were also found in patients with depression alone; 22 , 23 the decreased N -methylnicotinamide level and increased azelaic acid level were also found in patients with BD alone 19 . Meanwhile, Lin et al 56 reported that the levels of N -methylnicotinamide and hippuric acid were also significantly changed in patients with postpartum depression. Hou et al 57 also found the significantly altered N -methylnicotinamide and hippuric acid levels in hepatitis B patients with depression.…”

Section: Discussionmentioning

confidence: 99%

Urinary biomarker panel for diagnosing patients with depression and anxiety disorders

Chen

Bai

et al. 2018

Transl Psychiatry

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Available data indicate that patients with depression and anxiety disorders are likely to be at greater risk for suicide. Therefore, it is important to correctly diagnose patients with depression and anxiety disorders. However, there are still no empirical laboratory methods to objectively diagnose these patients. In this study, the multiple metabolomics platforms were used to profile the urine samples from 32 healthy controls and 32 patients with depression and anxiety disorders for identifying differential metabolites and potential biomarkers. Then, 16 healthy controls and 16 patients with depression and anxiety disorders were used to independently validate the diagnostic performance of the identified biomarkers. Finally, a panel consisting of four biomarkers—N-methylnicotinamide, aminomalonic acid, azelaic acid and hippuric acid—was identified. This panel was capable of distinguishing patients with depression and anxiety disorders from healthy controls with an area under the receiver operating characteristic curve of 0.977 in the training set and 0.934 in the testing set. Meanwhile, we found that these identified differential metabolites were mainly involved in three metabolic pathways and five molecular and cellular functions. Our results could lay the groundwork for future developing a urine-based diagnostic method for patients with depression and anxiety disorders.

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“…The higher abundance of glucose combined with the higher lactate to pyruvate ratio indicate alterations in both the tricarboxylic acid (TCA) and the Cori cycles which have been associated with metabolic stress conditions 46 . Lin et al 20 , reported also significant lactate abundance differences in the urine metabolomic study among postpartum depressed women, compared with both postpartum controls and non-pregnant controls. Plasma arginine, an amino acid, has previously been reported lower among women with antenatal depression in the first trimester, when compared with healthy controls 52 .…”

Section: Discussionmentioning

confidence: 92%

“…Metabolomics has previously been used in psychiatry research to study, e.g., metabolic profile in relation to major depressive disorder and posttraumatic stress disorder [16][17][18][19] , however, among women experiencing depression or depressive symptoms during the peripartum period the literature is scarcer. A study of urine metabolomics in postpartum depression identified a panel of five biomarkers (formate, succinate, 1-methylhistidin, α-glucose, and dimethylamine), which could distinguish postpartum depressed from postpartum non-depressed women, as well as from healthy controls (area under curve (AUC) in training set = 0.948 and in testing set = 0.944) 20 . In addition, a recent metabolomics study identified ten metabolites that had an altered abundance among postpartum depressed women compared with healthy controls 21 .…”

Section: Introductionmentioning

confidence: 99%

Blood plasma metabolic profiling of pregnant women with antenatal depressive symptoms

et al. 2019

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Antenatal depression affects~9-19% of pregnant women and can exert persistent adverse effects on both mother and child. There is a need for a deeper understanding of antenatal depression mechanisms and the development of tools for reliable diagnosis and early identification of women at high risk. As the use of untargeted blood metabolomics in the investigation of psychiatric and neurological diseases has increased substantially, the main objective of this study was to investigate whether untargeted gas chromatography-mass spectrometry (GC-MS) plasma metabolomics in 45 women in late pregnancy, residing in Uppsala, Sweden, could indicate metabolic differences between women with and without depressive symptoms. Furthermore, seasonal differences in the metabolic profiles were explored. When comparing the profiles of cases with controls, independently of season, no differences were observed. However, seasonal differences were observed in the metabolic profiles of control samples, suggesting a favorable cardiometabolic profile in the summer vs. winter, as indicated by lower glucose and sugar acid concentrations and lactate to pyruvate ratio, and higher abundance of arginine and phosphate. Similar differences were identified between cases and controls among summer pregnancies, indicating an association between a stressed metabolism and depressive symptoms. No depression-specific differences were apparent among depressed and non-depressed women, in the winter pregnancies; this could be attributed to an already stressed metabolism due to the winter living conditions. Our results provide new insights into the pathophysiology of antenatal depression, and warrant further investigation of the use of metabolomics in antenatal depression in larger cohorts.

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Novel urinary metabolite signature for diagnosing postpartum depression

Cited by 28 publications

References 37 publications

GC-MS-based metabolomics approach to diagnose depression in hepatitis B virus-infected patients with middle or old age

GC-MS-based metabolomics approach to diagnose depression in hepatitis B virus-infected patients with middle or old age

Urinary biomarker panel for diagnosing patients with depression and anxiety disorders

Blood plasma metabolic profiling of pregnant women with antenatal depressive symptoms

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